from sklearn.cluster import AgglomerativeClustering
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import scipy.cluster.hierarchy as sch
from sklearn.cluster import KMeans 
import seaborn as sns
from scipy import stats

mall_data=pd.read_csv(r"C:\Users\trish\Downloads\Mall_Customers.csv")
mall_data

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6
3	4	Female	23	16	77
4	5	Female	31	17	40
...	...	...	...	...	...
195	196	Female	35	120	79
196	197	Female	45	126	28
197	198	Male	32	126	74
198	199	Male	32	137	18
199	200	Male	30	137	83

200 rows × 5 columns

mall_data.sample(10)

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
75	76	Male	26	54	54
156	157	Male	37	78	1
142	143	Female	28	76	40
113	114	Male	19	64	46
184	185	Female	41	99	39
1	2	Male	21	15	81
49	50	Female	31	40	42
31	32	Female	21	30	73
158	159	Male	34	78	1

mall_data.describe()

	CustomerID	Age	Annual Income (k$)	Spending Score (1-100)
count	200.000000	200.000000	200.000000	200.000000
mean	100.500000	38.850000	60.560000	50.200000
std	57.879185	13.969007	26.264721	25.823522
min	1.000000	18.000000	15.000000	1.000000
25%	50.750000	28.750000	41.500000	34.750000
50%	100.500000	36.000000	61.500000	50.000000
75%	150.250000	49.000000	78.000000	73.000000
max	200.000000	70.000000	137.000000	99.000000

mall_data.isnull().sum()

CustomerID                0
Gender                    0
Age                       0
Annual Income (k$)        0
Spending Score (1-100)    0
dtype: int64

males_age = mall_data[mall_data['Gender']=='Male']['Age'] # subset with males age
females_age = mall_data[mall_data['Gender']=='Female']['Age'] # subset with females age

age_bins = range(15,75,5)

# males histogram
fig2, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,5), sharey=True)
sns.distplot(males_age, bins=age_bins, kde=False, color='#0066ff', ax=ax1, hist_kws=dict(edgecolor="k", linewidth=2))
ax1.set_xticks(age_bins)
ax1.set_ylim(top=25)
ax1.set_title('Males')
ax1.set_ylabel('Count')
ax1.text(45,23, "TOTAL count: {}".format(males_age.count()))
ax1.text(45,22, "Mean age: {:.1f}".format(males_age.mean()))

# females histogram
sns.distplot(females_age, bins=age_bins, kde=False, color='#cc66ff', ax=ax2, hist_kws=dict(edgecolor="k", linewidth=2))
ax2.set_xticks(age_bins)
ax2.set_title('Females')
ax2.set_ylabel('Count')
ax2.text(45,23, "TOTAL count: {}".format(females_age.count()))
ax2.text(45,22, "Mean age: {:.1f}".format(females_age.mean()))

plt.show()

print('Kolgomorov-Smirnov test p-value: {:.2f}'.format(stats.ks_2samp(males_age, females_age)[1]))

Kolgomorov-Smirnov test p-value: 0.49

def labeler(pct, allvals):
    absolute = int(pct/100.*np.sum(allvals))
    return "{:.1f}%\n({:d})".format(pct, absolute)

sizes = [males_age.count(),females_age.count()] # wedge sizes

fig0, ax1 = plt.subplots(figsize=(6,6))
wedges, texts, autotexts = ax1.pie(sizes,
                                   autopct=lambda pct: labeler(pct, sizes),
                                   radius=1,
                                   colors=['#0066ff','#cc66ff'],
                                   startangle=90,
                                   textprops=dict(color="w"),
                                   wedgeprops=dict(width=0.7, edgecolor='w'))

ax1.legend(wedges, ['male','female'],
           loc='center right',
           bbox_to_anchor=(0.7, 0, 0.5, 1))

plt.text(0,0, 'TOTAL\n{}'.format(mall_data['Age'].count()),
         weight='bold', size=12, color='#52527a',
         ha='center', va='center')

plt.setp(autotexts, size=12, weight='bold')
ax1.axis('equal')  # Equal aspect ratio
plt.show()

males_income = mall_data[mall_data['Gender']=='Male']['Annual Income (k$)'] # subset with males income
females_income = mall_data[mall_data['Gender']=='Female']['Annual Income (k$)'] # subset with females income

my_bins = range(10,150,10)

# males histogram
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18,5))
sns.distplot(males_income, bins=my_bins, kde=False, color='#0066ff', ax=ax1, hist_kws=dict(edgecolor="k", linewidth=2))
ax1.set_xticks(my_bins)
ax1.set_yticks(range(0,24,2))
ax1.set_ylim(0,22)
ax1.set_title('Males')
ax1.set_ylabel('Count')
ax1.text(85,19, "Mean income: {:.1f}k$".format(males_income.mean()))
ax1.text(85,18, "Median income: {:.1f}k$".format(males_income.median()))
ax1.text(85,17, "Std. deviation: {:.1f}k$".format(males_income.std()))

# females histogram
sns.distplot(females_income, bins=my_bins, kde=False, color='#cc66ff', ax=ax2, hist_kws=dict(edgecolor="k", linewidth=2))
ax2.set_xticks(my_bins)
ax2.set_yticks(range(0,24,2))
ax2.set_ylim(0,22)
ax2.set_title('Females')
ax2.set_ylabel('Count')
ax2.text(85,19, "Mean income: {:.1f}k$".format(females_income.mean()))
ax2.text(85,18, "Median income: {:.1f}k$".format(females_income.median()))
ax2.text(85,17, "Std. deviation: {:.1f}k$".format(females_income.std()))

# boxplot
sns.boxplot(x='Gender', y='Annual Income (k$)', data=mall_data, ax=ax3)
ax3.set_title('Boxplot of annual income')
plt.show()

print('Kolgomorov-Smirnov test p-value: {:.2f}'.format(stats.ks_2samp(males_income, females_income)[1]))

Kolgomorov-Smirnov test p-value: 0.78

males_spending = mall_data[mall_data['Gender']=='Male']['Spending Score (1-100)'] # subset with males age
females_spending = mall_data[mall_data['Gender']=='Female']['Spending Score (1-100)'] # subset with females age

spending_bins = range(0,105,5)

# males histogram
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18,5))
sns.distplot(males_spending, bins=spending_bins, kde=False, color='#0066ff', ax=ax1, hist_kws=dict(edgecolor="k", linewidth=2))
ax1.set_xticks(spending_bins)
ax1.set_xlim(0,100)
ax1.set_yticks(range(0,17,1))
ax1.set_ylim(0,16)
ax1.set_title('Males')
ax1.set_ylabel('Count')
ax1.text(50,15, "Mean spending score: {:.1f}".format(males_spending.mean()))
ax1.text(50,14, "Median spending score: {:.1f}".format(males_spending.median()))
ax1.text(50,13, "Std. deviation score: {:.1f}".format(males_spending.std()))

# females histogram
sns.distplot(females_spending, bins=spending_bins, kde=False, color='#cc66ff', ax=ax2, hist_kws=dict(edgecolor="k", linewidth=2))
ax2.set_xticks(spending_bins)
ax2.set_xlim(0,100)
ax2.set_yticks(range(0,17,1))
ax2.set_ylim(0,16)
ax2.set_title('Females')
ax2.set_ylabel('Count')
ax2.text(50,15, "Mean spending score: {:.1f}".format(females_spending.mean()))
ax2.text(50,14, "Median spending score: {:.1f}".format(females_spending.median()))
ax2.text(50,13, "Std. deviation score: {:.1f}".format(females_spending.std()))

# boxplot
sns.boxplot(x='Gender', y='Spending Score (1-100)', data=mall_data, ax=ax3)
ax3.set_title('Boxplot of spending score')
plt.show()

plt.show()

medians_by_age_group = mall_data.groupby(["Gender",pd.cut(mall_data['Age'], age_bins)]).median()
medians_by_age_group.index = medians_by_age_group.index.set_names(['Gender', 'Age_group'])
medians_by_age_group.reset_index(inplace=True)

fig, ax = plt.subplots(figsize=(12,5))
sns.barplot(x='Age_group', y='Annual Income (k$)', hue='Gender', data=medians_by_age_group,
            palette=['#cc66ff','#0066ff'],
            alpha=0.7,edgecolor='k',
            ax=ax)
ax.set_title('Median annual income of male and female customers')
ax.set_xlabel('Age group')
plt.show()

from scipy.stats import pearsonr

# calculating Pearson's correlation
corr, _ = pearsonr(mall_data['Age'], mall_data['Spending Score (1-100)'])

jp = (sns.jointplot('Age', 'Spending Score (1-100)', data=mall_data,
                    kind='reg')).plot_joint(sns.kdeplot, zorder=0, n_levels=6)

plt.text(0,120, 'Pearson: {:.2f}'.format(corr))
plt.show()

C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

corr1, _ = pearsonr(males_age.values, males_income.values)
corr2, _ = pearsonr(females_age.values, females_income.values)

sns.lmplot('Age', 'Annual Income (k$)', data=mall_data, hue='Gender',
          aspect=1.5)

plt.text(15,87, 'Pearson: {:.2f}'.format(corr1), color='blue')
plt.text(65,80, 'Pearson: {:.2f}'.format(corr2), color='orange')

plt.show()

C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

corr1, _ = pearsonr(males_age.values, males_spending.values)
corr2, _ = pearsonr(females_age.values, females_spending.values)

sns.lmplot('Age', 'Spending Score (1-100)', data=mall_data, hue='Gender',
          aspect=1.5)

plt.text(65,65, 'Pearson: {:.2f}'.format(corr1), color='blue')
plt.text(13,83, 'Pearson: {:.2f}'.format(corr2), color='#d97900')

plt.show()

C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

# calculating Pearson's correlations
corr1, _ = pearsonr(males_income.values, males_spending.values)
corr2, _ = pearsonr(females_income.values, females_spending.values)

sns.lmplot('Annual Income (k$)', 'Spending Score (1-100)', data=mall_data, hue='Gender',
          aspect=1.5)

plt.text(130,23, 'Pearson: {:.2f}'.format(corr1), color='blue')
plt.text(130,77, 'Pearson: {:.2f}'.format(corr2), color='#d97900')

plt.show()

C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

plt.figure(1 , figsize = (15 , 6))
n = 0 
for x in ['Age' , 'Annual Income (k$)' , 'Spending Score (1-100)']:
    n += 1
    plt.subplot(1 , 3 , n)
    plt.subplots_adjust(hspace = 0.5 , wspace = 0.5)
    sns.distplot(mall_data[x] , bins = 15)
    plt.title('Distplot of {}'.format(x))
plt.show()

#K-MEANS CLUSTERING

X_numerics = mall_data[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']] 

from yellowbrick.cluster import KElbowVisualizer

model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(2,10))

visualizer.fit(X_numerics)
visualizer.show()
plt.show()

model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(2,10), metric='silhouette')

visualizer.fit(X_numerics)
visualizer.show()
plt.show()

KM_5_clusters = KMeans(n_clusters=5, init='k-means++').fit(X_numerics) # initialise and fit K-Means model

KM5_clustered = X_numerics.copy()
KM5_clustered.loc[:,'Cluster'] = KM_5_clusters.labels_ # append labels to points

fig1, (axes) = plt.subplots(1,2,figsize=(12,5))


scat_1 = sns.scatterplot('Annual Income (k$)', 'Spending Score (1-100)', data=KM5_clustered,
                hue='Cluster', ax=axes[0], palette='Set1', legend='full')

sns.scatterplot('Age', 'Spending Score (1-100)', data=KM5_clustered,
                hue='Cluster', palette='Set1', ax=axes[1], legend='full')

axes[0].scatter(KM_5_clusters.cluster_centers_[:,1],KM_5_clusters.cluster_centers_[:,2], marker='s', s=40, c="blue")
axes[1].scatter(KM_5_clusters.cluster_centers_[:,0],KM_5_clusters.cluster_centers_[:,2], marker='s', s=40, c="blue")
plt.show()

C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

KM_clust_sizes = KM5_clustered.groupby('Cluster').size().to_frame()
KM_clust_sizes.columns = ["KM_size"]
KM_clust_sizes

	KM_size
Cluster
0	79
1	39
2	23
3	36
4	23

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(7, 7))
ax = Axes3D(fig, rect=[0, 0, .99, 1], elev=20, azim=210)
ax.scatter(KM5_clustered['Age'],
           KM5_clustered['Annual Income (k$)'],
           KM5_clustered['Spending Score (1-100)'],
           c=KM5_clustered['Cluster'],
           s=35, edgecolor='k', cmap=plt.cm.Set1)

ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
ax.set_xlabel('Age')
ax.set_ylabel('Annual Income (k$)')
ax.set_zlabel('Spending Score (1-100)')
ax.set_title('3D view of K-Means 5 clusters')
ax.dist = 12

plt.show()

C:\Users\trish\AppData\Local\Temp/ipykernel_33004/3433933387.py:4: MatplotlibDeprecationWarning: Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes.
  ax = Axes3D(fig, rect=[0, 0, .99, 1], elev=20, azim=210)

import plotly as py
import plotly.graph_objs as go

def tracer(db, n, name):
    '''
    This function returns trace object for Plotly
    '''
    return go.Scatter3d(
        x = db[db['Cluster']==n]['Age'],
        y = db[db['Cluster']==n]['Spending Score (1-100)'],
        z = db[db['Cluster']==n]['Annual Income (k$)'],
        mode = 'markers',
        name = name,
        marker = dict(
            size = 5
        )
     )

trace0 = tracer(KM5_clustered, 0, 'Cluster 0')
trace1 = tracer(KM5_clustered, 1, 'Cluster 1')
trace2 = tracer(KM5_clustered, 2, 'Cluster 2')
trace3 = tracer(KM5_clustered, 3, 'Cluster 3')
trace4 = tracer(KM5_clustered, 4, 'Cluster 4')

data = [trace0, trace1, trace2, trace3, trace4]

layout = go.Layout(
    title = 'Clusters by K-Means',
    scene = dict(
            xaxis = dict(title = 'Age'),
            yaxis = dict(title = 'Spending Score'),
            zaxis = dict(title = 'Annual Income')
        )
)

fig = go.Figure(data=data, layout=layout)
py.offline.iplot(fig)

from yellowbrick.cluster import SilhouetteVisualizer
model = KMeans(n_clusters=5, random_state=0)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
visualizer.fit(X_numerics)
visualizer.show()
plt.show()

KM_6_clusters = KMeans(n_clusters=6, init='k-means++').fit(X_numerics) # initialise and fit K-Means model

KM6_clustered = X_numerics.copy()
KM6_clustered.loc[:,'Cluster'] = KM_6_clusters.labels_ # append labels to points

fig11, (axes) = plt.subplots(1,2,figsize=(12,5))

sns.scatterplot('Annual Income (k$)', 'Spending Score (1-100)', data=KM6_clustered,
                hue='Cluster', ax=axes[0], palette='Set1', legend='full')

sns.scatterplot('Age', 'Spending Score (1-100)', data=KM6_clustered,
                hue='Cluster', palette='Set1', ax=axes[1], legend='full')

# plotting centroids
axes[0].scatter(KM_6_clusters.cluster_centers_[:,1], KM_6_clusters.cluster_centers_[:,2], marker='s', s=40, c="blue")
axes[1].scatter(KM_6_clusters.cluster_centers_[:,0], KM_6_clusters.cluster_centers_[:,2], marker='s', s=40, c="blue")
plt.show()

C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

KM6_clust_sizes = KM6_clustered.groupby('Cluster').size().to_frame()
KM6_clust_sizes.columns = ["KM_size"]
KM6_clust_sizes

	KM_size
Cluster
0	22
1	39
2	35
3	21
4	38
5	45

trace0 = tracer(KM6_clustered, 0, 'Cluster 0')
trace1 = tracer(KM6_clustered, 1, 'Cluster 1')
trace2 = tracer(KM6_clustered, 2, 'Cluster 2')
trace3 = tracer(KM6_clustered, 3, 'Cluster 3')
trace4 = tracer(KM6_clustered, 4, 'Cluster 4')
trace5 = tracer(KM6_clustered, 5, 'Cluster 5')

data = [trace0, trace1, trace2, trace3, trace4, trace5]

layout = go.Layout(
    title = 'Clusters by K-Means',
    scene = dict(
            xaxis = dict(title = 'Age'),
            yaxis = dict(title = 'Spending Score'),
            zaxis = dict(title = 'Annual Income')
        )
)

fig = go.Figure(data=data, layout=layout)
py.offline.iplot(fig)

model = KMeans(n_clusters=6, random_state=0)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
visualizer.fit(X_numerics)
visualizer.show()
plt.show()

#DBSCAN CLUSTERING

X_numerics = mall_data[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]

from sklearn.cluster import DBSCAN

from itertools import product
eps_values = np.arange(8,12.75,0.25)
min_samples=np.arange(3,10)
DBSCAN_params=list(product(eps_values,min_samples))

from sklearn.metrics import silhouette_score
no_of_clusters=[]
sil_score=[]
for p in DBSCAN_params:
    DBS_clustering = DBSCAN(eps=p[0],min_samples=p[1]).fit(X_numerics)
    no_of_clusters.append(len(np.unique(DBS_clustering.labels_)))
    sil_score.append(silhouette_score(X_numerics,DBS_clustering.labels_))

tmp = pd.DataFrame.from_records(DBSCAN_params, columns =['Eps', 'Min_samples'])   
tmp['No_of_clusters'] = no_of_clusters

pivot_1 = pd.pivot_table(tmp, values='No_of_clusters', index='Min_samples', columns='Eps')

fig, ax = plt.subplots(figsize=(12,6))
sns.heatmap(pivot_1, annot=True,annot_kws={"size": 16}, cmap="YlGnBu", ax=ax)
ax.set_title('Number of clusters')
plt.show()

tmp = pd.DataFrame.from_records(DBSCAN_params, columns =['Eps', 'Min_samples'])   
tmp['Sil_score'] = sil_score

pivot_1 = pd.pivot_table(tmp, values='Sil_score', index='Min_samples', columns='Eps')

fig, ax = plt.subplots(figsize=(18,6))
sns.heatmap(pivot_1, annot=True, annot_kws={"size": 10}, cmap="YlGnBu", ax=ax)
plt.show()

DBS_clustering = DBSCAN(eps=12.5, min_samples=4).fit(X_numerics)

DBSCAN_clustered = X_numerics.copy()
DBSCAN_clustered.loc[:,'Cluster'] = DBS_clustering.labels_

DBSCAN_clust_sizes = DBSCAN_clustered.groupby('Cluster').size().to_frame()
DBSCAN_clust_sizes.columns = ["DBSCAN_size"]
DBSCAN_clust_sizes

	DBSCAN_size
Cluster
-1	18
0	112
1	8
2	34
3	24
4	4

outliers = DBSCAN_clustered[DBSCAN_clustered['Cluster']==-1]

fig2, (axes) = plt.subplots(1,2,figsize=(12,5))


sns.scatterplot('Annual Income (k$)', 'Spending Score (1-100)',
                data=DBSCAN_clustered[DBSCAN_clustered['Cluster']!=-1],
                hue='Cluster', ax=axes[0], palette='Set1', legend='full', s=45)

sns.scatterplot('Age', 'Spending Score (1-100)',
                data=DBSCAN_clustered[DBSCAN_clustered['Cluster']!=-1],
                hue='Cluster', palette='Set1', ax=axes[1], legend='full', s=45)

axes[0].scatter(outliers['Annual Income (k$)'], outliers['Spending Score (1-100)'], s=5, label='outliers', c="k")
axes[1].scatter(outliers['Age'], outliers['Spending Score (1-100)'], s=5, label='outliers', c="k")
axes[0].legend()
axes[1].legend()

plt.setp(axes[0].get_legend().get_texts(), fontsize='10')
plt.setp(axes[1].get_legend().get_texts(), fontsize='10')

plt.show()

C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

C:\Users\trish\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

#HIERARCHICAL CLUSTERING(AGGLOMERATIVE CLUSTERING)

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns

import plotly as py
import plotly.graph_objs as go

import warnings
warnings.filterwarnings('ignore')

from sklearn import preprocessing 
import scipy.cluster.hierarchy as sch
from sklearn.cluster import AgglomerativeClustering 

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6
3	4	Female	23	16	77
4	5	Female	31	17	40

label_encoder = preprocessing.LabelEncoder() 

mall_data['Gender'] = label_encoder.fit_transform(mall_data['Gender'])
mall_data.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	1	19	15	39
1	2	1	21	15	81
2	3	0	20	16	6
3	4	0	23	16	77
4	5	0	31	17	40

plt.figure(1, figsize = (16 ,8))
dendrogram = sch.dendrogram(sch.linkage(mall_data, method  = "ward"))

plt.title('Dendrogram')
plt.xlabel('Customers')
plt.ylabel('Euclidean distances')
plt.show()

hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage ='average')

y_hc = hc.fit_predict(mall_data)
y_hc

array([3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4,
       3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 2,
       3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 1, 2, 1, 0, 1, 0, 1,
       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
       0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
       0, 1], dtype=int64)

mall_data['cluster'] = pd.DataFrame(y_hc)

trace1 = go.Scatter3d(
    x= mall_data['Age'],
    y= mall_data['Spending Score (1-100)'],
    z= mall_data['Annual Income (k$)'],
    mode='markers',
     marker=dict(
        color = mall_data['cluster'], 
        size= 10,
        line=dict(
            color= mall_data['cluster'],
            width= 12
        ),
        opacity=0.8
     )
)
data = [trace1]
layout = go.Layout(
    title= 'Clusters using Agglomerative Clustering',
    scene = dict(
            xaxis = dict(title  = 'Age'),
            yaxis = dict(title  = 'Spending Score'),
            zaxis = dict(title  = 'Annual Income')
        )
)
fig = go.Figure(data=data, layout=layout)
py.offline.iplot(fig)

X = mall_data.iloc[:, [3,4]].values
plt.scatter(X[y_hc==0, 0], X[y_hc==0, 1], s=100, c='red', label ='Cluster 1')
plt.scatter(X[y_hc==1, 0], X[y_hc==1, 1], s=100, c='blue', label ='Cluster 2')
plt.scatter(X[y_hc==2, 0], X[y_hc==2, 1], s=100, c='green', label ='Cluster 3')
plt.scatter(X[y_hc==3, 0], X[y_hc==3, 1], s=100, c='purple', label ='Cluster 4')
plt.scatter(X[y_hc==4, 0], X[y_hc==4, 1], s=100, c='orange', label ='Cluster 5')
plt.title('Clusters of Customers (Hierarchical Clustering Model)')
plt.xlabel('Annual Income(k$)')
plt.ylabel('Spending Score(1-100)')
plt.show()